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</button>
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<span class="navbar-brand">
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<a class="navbar-link" href="../index.html">AMR (for R)</a>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0</span>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9008</span>
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</span>
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</div>
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@ -187,7 +187,8 @@
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</header><script src="PCA_files/header-attrs-2.3/header-attrs.js"></script><script src="PCA_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
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</header><script src="PCA_files/accessible-code-block-0.0.1/empty-anchor.js"></script><link href="PCA_files/anchor-sections-1.0/anchor-sections.css" rel="stylesheet">
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<script src="PCA_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row">
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<div class="col-md-9 contents">
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<div class="page-header toc-ignore">
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<h1 data-toc-skip>How to conduct principal component analysis (PCA) for AMR</h1>
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@ -210,9 +211,9 @@
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<a href="#transforming" class="anchor"></a>Transforming</h1>
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<p>For PCA, we need to transform our AMR data first. This is what the <code>example_isolates</code> data set in this package looks like:</p>
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<div class="sourceCode" id="cb1"><pre class="downlit">
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<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://msberends.github.io/AMR">AMR</a></span>)
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<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://dplyr.tidyverse.org">dplyr</a></span>)
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<span class="fu"><a href="https://tibble.tidyverse.org/reference/glimpse.html">glimpse</a></span>(<span class="kw">example_isolates</span>)
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<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
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<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span>
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<span class="fu"><a href="https://tibble.tidyverse.org/reference/glimpse.html">glimpse</a></span><span class="op">(</span><span class="va">example_isolates</span><span class="op">)</span>
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<span class="co"># Rows: 2,000</span>
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<span class="co"># Columns: 49</span>
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<span class="co"># $ date <date> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002…</span>
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@ -263,18 +264,17 @@
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<span class="co"># $ CHL <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
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<span class="co"># $ COL <rsi> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, …</span>
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<span class="co"># $ MUP <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
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<span class="co"># $ RIF <rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span>
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</pre></div>
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<span class="co"># $ RIF <rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span></pre></div>
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<p>Now to transform this to a data set with only resistance percentages per taxonomic order and genus:</p>
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<div class="sourceCode" id="cb2"><pre class="downlit">
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<span class="kw">resistance_data</span> <span class="op"><-</span> <span class="kw">example_isolates</span> <span class="op">%>%</span>
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<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span>(order = <span class="fu"><a href="../reference/mo_property.html">mo_order</a></span>(<span class="kw">mo</span>), <span class="co"># group on anything, like order</span>
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genus = <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span>(<span class="kw">mo</span>)) <span class="op">%>%</span> <span class="co"># and genus as we do here</span>
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<span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise_all.html">summarise_if</a></span>(<span class="kw">is.rsi</span>, <span class="kw">resistance</span>) <span class="op">%>%</span> <span class="co"># then get resistance of all drugs</span>
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<span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span>(<span class="kw">order</span>, <span class="kw">genus</span>, <span class="kw">AMC</span>, <span class="kw">CXM</span>, <span class="kw">CTX</span>,
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<span class="kw">CAZ</span>, <span class="kw">GEN</span>, <span class="kw">TOB</span>, <span class="kw">TMP</span>, <span class="kw">SXT</span>) <span class="co"># and select only relevant columns</span>
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<span class="va">resistance_data</span> <span class="op"><-</span> <span class="va">example_isolates</span> <span class="op">%>%</span>
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<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span><span class="op">(</span>order <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_order</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, <span class="co"># group on anything, like order</span>
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genus <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span><span class="op">)</span> <span class="op">%>%</span> <span class="co"># and genus as we do here</span>
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<span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise_all.html">summarise_if</a></span><span class="op">(</span><span class="va">is.rsi</span>, <span class="va">resistance</span><span class="op">)</span> <span class="op">%>%</span> <span class="co"># then get resistance of all drugs</span>
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<span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span><span class="op">(</span><span class="va">order</span>, <span class="va">genus</span>, <span class="va">AMC</span>, <span class="va">CXM</span>, <span class="va">CTX</span>,
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<span class="va">CAZ</span>, <span class="va">GEN</span>, <span class="va">TOB</span>, <span class="va">TMP</span>, <span class="va">SXT</span><span class="op">)</span> <span class="co"># and select only relevant columns</span>
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<span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span>(<span class="kw">resistance_data</span>)
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<span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span>
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<span class="co"># # A tibble: 6 x 10</span>
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<span class="co"># # Groups: order [2]</span>
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<span class="co"># order genus AMC CXM CTX CAZ GEN TOB TMP SXT</span>
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@ -284,46 +284,40 @@
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<span class="co"># 3 Actinomycetales Cutibacterium NA NA NA NA NA NA NA NA</span>
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<span class="co"># 4 Actinomycetales Dermabacter NA NA NA NA NA NA NA NA</span>
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<span class="co"># 5 Actinomycetales Micrococcus NA NA NA NA NA NA NA NA</span>
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<span class="co"># 6 Actinomycetales Rothia NA NA NA NA NA NA NA NA</span>
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</pre></div>
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<span class="co"># 6 Actinomycetales Rothia NA NA NA NA NA NA NA NA</span></pre></div>
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</div>
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<div id="perform-principal-component-analysis" class="section level1">
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<h1 class="hasAnchor">
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<a href="#perform-principal-component-analysis" class="anchor"></a>Perform principal component analysis</h1>
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<p>The new <code><a href="../reference/pca.html">pca()</a></code> function will automatically filter on rows that contain numeric values in all selected variables, so we now only need to do:</p>
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<div class="sourceCode" id="cb3"><pre class="downlit">
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<span class="kw">pca_result</span> <span class="op"><-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span>(<span class="kw">resistance_data</span>)
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<span class="va">pca_result</span> <span class="op"><-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span>
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<span class="co"># NOTE: Columns selected for PCA: AMC CXM CTX CAZ GEN TOB TMP SXT.</span>
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<span class="co"># Total observations available: 7.</span>
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</pre></div>
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<span class="co"># Total observations available: 7.</span></pre></div>
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<p>The result can be reviewed with the good old <code><a href="https://rdrr.io/r/base/summary.html">summary()</a></code> function:</p>
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<div class="sourceCode" id="cb4"><pre class="downlit">
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<span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(<span class="kw">pca_result</span>)
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<span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span>
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<span class="co"># Importance of components:</span>
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<span class="co"># PC1 PC2 PC3 PC4 PC5 PC6 PC7</span>
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<span class="co"># Standard deviation 2.154 1.6807 0.61365 0.33902 0.20757 0.03136 1.733e-16</span>
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<span class="co"># Proportion of Variance 0.580 0.3531 0.04707 0.01437 0.00539 0.00012 0.000e+00</span>
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<span class="co"># Cumulative Proportion 0.580 0.9331 0.98012 0.99449 0.99988 1.00000 1.000e+00</span>
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</pre></div>
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<span class="co"># Cumulative Proportion 0.580 0.9331 0.98012 0.99449 0.99988 1.00000 1.000e+00</span></pre></div>
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<p>Good news. The first two components explain a total of 93.3% of the variance (see the PC1 and PC2 values of the <em>Proportion of Variance</em>. We can create a so-called biplot with the base R <code><a href="https://rdrr.io/r/stats/biplot.html">biplot()</a></code> function, to see which antimicrobial resistance per drug explain the difference per microorganism.</p>
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</div>
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<div id="plotting-the-results" class="section level1">
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<h1 class="hasAnchor">
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<a href="#plotting-the-results" class="anchor"></a>Plotting the results</h1>
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<div class="sourceCode" id="cb5"><pre class="downlit">
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<span class="fu"><a href="https://rdrr.io/r/stats/biplot.html">biplot</a></span>(<span class="kw">pca_result</span>)
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</pre></div>
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<span class="fu"><a href="https://rdrr.io/r/stats/biplot.html">biplot</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></pre></div>
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<p><img src="PCA_files/figure-html/unnamed-chunk-5-1.png" width="750"></p>
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<p>But we can’t see the explanation of the points. Perhaps this works better with our new <code><a href="../reference/ggplot_pca.html">ggplot_pca()</a></code> function, that automatically adds the right labels and even groups:</p>
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<div class="sourceCode" id="cb6"><pre class="downlit">
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<span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span>(<span class="kw">pca_result</span>)
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</pre></div>
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<span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></pre></div>
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<p><img src="PCA_files/figure-html/unnamed-chunk-6-1.png" width="750"></p>
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<p>You can also print an ellipse per group, and edit the appearance:</p>
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<div class="sourceCode" id="cb7"><pre class="downlit">
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<span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span>(<span class="kw">pca_result</span>, ellipse = <span class="fl">TRUE</span>) <span class="op">+</span>
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<span class="kw">ggplot2</span>::<span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html">labs</a></span>(title = <span class="st">"An AMR/PCA biplot!"</span>)
|
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</pre></div>
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<span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span>, ellipse <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">+</span>
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<span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"An AMR/PCA biplot!"</span><span class="op">)</span></pre></div>
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<p><img src="PCA_files/figure-html/unnamed-chunk-7-1.png" width="750"></p>
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</div>
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</div>
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</div>
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<div class="pkgdown">
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<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.9000.</p>
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<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
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</div>
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</footer>
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/* Styles for section anchors */
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a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
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a.anchor-section::before {content: '#';}
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.hasAnchor:hover a.anchor-section {visibility: visible;}
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// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
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document.addEventListener('DOMContentLoaded', function() {
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// Do nothing if AnchorJS is used
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if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
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// Do nothing if sections are already anchored
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// Use section id when pandoc runs with --section-divs
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const section_id = function(x) {
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return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
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@ -39,7 +39,7 @@
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</button>
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<span class="navbar-brand">
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<a class="navbar-link" href="../index.html">AMR (for R)</a>
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||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0</span>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9008</span>
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</span>
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</div>
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@ -187,13 +187,14 @@
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</header><script src="SPSS_files/header-attrs-2.3/header-attrs.js"></script><script src="SPSS_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
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||||
</header><script src="SPSS_files/accessible-code-block-0.0.1/empty-anchor.js"></script><link href="SPSS_files/anchor-sections-1.0/anchor-sections.css" rel="stylesheet">
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<script src="SPSS_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row">
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<div class="col-md-9 contents">
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||||
<div class="page-header toc-ignore">
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||||
<h1 data-toc-skip>How to import data from SPSS / SAS / Stata</h1>
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<h4 class="author">Matthijs S. Berends</h4>
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<h4 class="date">08 October 2020</h4>
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<h4 class="date">26 October 2020</h4>
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<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/SPSS.Rmd"><code>vignettes/SPSS.Rmd</code></a></small>
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<div class="hidden name"><code>SPSS.Rmd</code></div>
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@ -249,38 +250,37 @@
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<p>To demonstrate the first point:</p>
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<div class="sourceCode" id="cb1"><pre class="downlit">
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<span class="co"># not all values are valid MIC values:</span>
|
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<span class="fu"><a href="../reference/as.mic.html">as.mic</a></span>(<span class="fl">0.125</span>)
|
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<span class="fu"><a href="../reference/as.mic.html">as.mic</a></span><span class="op">(</span><span class="fl">0.125</span><span class="op">)</span>
|
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<span class="co"># Class <mic></span>
|
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<span class="co"># [1] 0.125</span>
|
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<span class="fu"><a href="../reference/as.mic.html">as.mic</a></span>(<span class="st">"testvalue"</span>)
|
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<span class="fu"><a href="../reference/as.mic.html">as.mic</a></span><span class="op">(</span><span class="st">"testvalue"</span><span class="op">)</span>
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<span class="co"># Class <mic></span>
|
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<span class="co"># [1] <NA></span>
|
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<span class="co"># the Gram stain is avaiable for all bacteria:</span>
|
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<span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span>(<span class="st">"E. coli"</span>)
|
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<span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="st">"E. coli"</span><span class="op">)</span>
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<span class="co"># [1] "Gram-negative"</span>
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<span class="co"># Klebsiella is intrinsic resistant to amoxicllin, according to EUCAST:</span>
|
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<span class="kw">klebsiella_test</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span>(mo = <span class="st">"klebsiella"</span>,
|
||||
amox = <span class="st">"S"</span>,
|
||||
stringsAsFactors = <span class="fl">FALSE</span>)
|
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<span class="kw">klebsiella_test</span> <span class="co"># (our original data)</span>
|
||||
<span class="va">klebsiella_test</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="st">"klebsiella"</span>,
|
||||
amox <span class="op">=</span> <span class="st">"S"</span>,
|
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stringsAsFactors <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>
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<span class="va">klebsiella_test</span> <span class="co"># (our original data)</span>
|
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<span class="co"># mo amox</span>
|
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<span class="co"># 1 klebsiella S</span>
|
||||
<span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span>(<span class="kw">klebsiella_test</span>, info = <span class="fl">FALSE</span>) <span class="co"># (the edited data by EUCAST rules)</span>
|
||||
<span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">klebsiella_test</span>, info <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span> <span class="co"># (the edited data by EUCAST rules)</span>
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<span class="co"># mo amox</span>
|
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<span class="co"># 1 klebsiella R</span>
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|
||||
<span class="co"># hundreds of trade names can be translated to a name, trade name or an ATC code:</span>
|
||||
<span class="fu"><a href="../reference/ab_property.html">ab_name</a></span>(<span class="st">"floxapen"</span>)
|
||||
<span class="fu"><a href="../reference/ab_property.html">ab_name</a></span><span class="op">(</span><span class="st">"floxapen"</span><span class="op">)</span>
|
||||
<span class="co"># [1] "Flucloxacillin"</span>
|
||||
<span class="fu"><a href="../reference/ab_property.html">ab_tradenames</a></span>(<span class="st">"floxapen"</span>)
|
||||
<span class="fu"><a href="../reference/ab_property.html">ab_tradenames</a></span><span class="op">(</span><span class="st">"floxapen"</span><span class="op">)</span>
|
||||
<span class="co"># [1] "floxacillin" "floxapen" "floxapen sodium salt"</span>
|
||||
<span class="co"># [4] "fluclox" "flucloxacilina" "flucloxacillin" </span>
|
||||
<span class="co"># [7] "flucloxacilline" "flucloxacillinum" "fluorochloroxacillin"</span>
|
||||
<span class="fu"><a href="../reference/ab_property.html">ab_atc</a></span>(<span class="st">"floxapen"</span>)
|
||||
<span class="co"># [1] "J01CF05"</span>
|
||||
</pre></div>
|
||||
<span class="fu"><a href="../reference/ab_property.html">ab_atc</a></span><span class="op">(</span><span class="st">"floxapen"</span><span class="op">)</span>
|
||||
<span class="co"># [1] "J01CF05"</span></pre></div>
|
||||
</div>
|
||||
<div id="import-data-from-spsssasstata" class="section level2">
|
||||
<h2 class="hasAnchor">
|
||||
@ -294,10 +294,10 @@
|
||||
<p>If additional packages are needed, RStudio will ask you if they should be installed on beforehand.</p>
|
||||
<p>In the the window that opens, you can define all options (parameters) that should be used for import and you’re ready to go:</p>
|
||||
<p><img src="https://github.com/msberends/AMR/raw/master/docs/import2.png"></p>
|
||||
<p>If you want named variables to be imported as factors so it resembles SPSS more, use <code><a href="https://haven.tidyverse.org/reference/as_factor.html">as_factor()</a></code>.</p>
|
||||
<p>If you want named variables to be imported as factors so it resembles SPSS more, use <code>as_factor()</code>.</p>
|
||||
<p>The difference is this:</p>
|
||||
<div class="sourceCode" id="cb2"><pre class="downlit">
|
||||
<span class="kw">SPSS_data</span>
|
||||
<span class="va">SPSS_data</span>
|
||||
<span class="co"># # A tibble: 4,203 x 4</span>
|
||||
<span class="co"># v001 sex status statusage</span>
|
||||
<span class="co"># <dbl> <dbl+lbl> <dbl+lbl> <dbl></span>
|
||||
@ -313,7 +313,7 @@
|
||||
<span class="co"># 10 10018 0 1 66.6</span>
|
||||
<span class="co"># # … with 4,193 more rows</span>
|
||||
|
||||
<span class="fu">as_factor</span>(<span class="kw">SPSS_data</span>)
|
||||
<span class="fu">as_factor</span><span class="op">(</span><span class="va">SPSS_data</span><span class="op">)</span>
|
||||
<span class="co"># # A tibble: 4,203 x 4</span>
|
||||
<span class="co"># v001 sex status statusage</span>
|
||||
<span class="co"># <dbl> <fct> <fct> <dbl></span>
|
||||
@ -327,8 +327,7 @@
|
||||
<span class="co"># 8 10011 Male alive 73.1</span>
|
||||
<span class="co"># 9 10017 Male alive 56.7</span>
|
||||
<span class="co"># 10 10018 Female alive 66.6</span>
|
||||
<span class="co"># # … with 4,193 more rows</span>
|
||||
</pre></div>
|
||||
<span class="co"># # … with 4,193 more rows</span></pre></div>
|
||||
</div>
|
||||
<div id="base-r" class="section level3">
|
||||
<h3 class="hasAnchor">
|
||||
@ -336,10 +335,9 @@
|
||||
<p>To import data from SPSS, SAS or Stata, you can use the <a href="https://haven.tidyverse.org/">great <code>haven</code> package</a> yourself:</p>
|
||||
<div class="sourceCode" id="cb3"><pre class="downlit">
|
||||
<span class="co"># download and install the latest version:</span>
|
||||
<span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html">install.packages</a></span>(<span class="st">"haven"</span>)
|
||||
<span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html">install.packages</a></span><span class="op">(</span><span class="st">"haven"</span><span class="op">)</span>
|
||||
<span class="co"># load the package you just installed:</span>
|
||||
<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="http://haven.tidyverse.org">haven</a></span>)
|
||||
</pre></div>
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va">haven</span><span class="op">)</span> </pre></div>
|
||||
<p>You can now import files as follows:</p>
|
||||
<div id="spss" class="section level4">
|
||||
<h4 class="hasAnchor">
|
||||
@ -347,23 +345,21 @@
|
||||
<p>To read files from SPSS into R:</p>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit">
|
||||
<span class="co"># read any SPSS file based on file extension (best way):</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_spss</a></span>(file = <span class="st">"path/to/file"</span>)
|
||||
<span class="fu">read_spss</span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
|
||||
|
||||
<span class="co"># read .sav or .zsav file:</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_sav</a></span>(file = <span class="st">"path/to/file"</span>)
|
||||
<span class="fu">read_sav</span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
|
||||
|
||||
<span class="co"># read .por file:</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_por</a></span>(file = <span class="st">"path/to/file"</span>)
|
||||
</pre></div>
|
||||
<p>Do not forget about <code><a href="https://haven.tidyverse.org/reference/as_factor.html">as_factor()</a></code>, as mentioned above.</p>
|
||||
<span class="fu">read_por</span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span></pre></div>
|
||||
<p>Do not forget about <code>as_factor()</code>, as mentioned above.</p>
|
||||
<p>To export your R objects to the SPSS file format:</p>
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit">
|
||||
<span class="co"># save as .sav file:</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">write_sav</a></span>(data = <span class="kw">yourdata</span>, path = <span class="st">"path/to/file"</span>)
|
||||
<span class="fu">write_sav</span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
|
||||
|
||||
<span class="co"># save as compressed .zsav file:</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">write_sav</a></span>(data = <span class="kw">yourdata</span>, path = <span class="st">"path/to/file"</span>, compress = <span class="fl">TRUE</span>)
|
||||
</pre></div>
|
||||
<span class="fu">write_sav</span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span>, compress <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></pre></div>
|
||||
</div>
|
||||
<div id="sas" class="section level4">
|
||||
<h4 class="hasAnchor">
|
||||
@ -371,20 +367,18 @@
|
||||
<p>To read files from SAS into R:</p>
|
||||
<div class="sourceCode" id="cb6"><pre class="downlit">
|
||||
<span class="co"># read .sas7bdat + .sas7bcat files:</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_sas.html">read_sas</a></span>(data_file = <span class="st">"path/to/file"</span>, catalog_file = <span class="kw">NULL</span>)
|
||||
<span class="fu">read_sas</span><span class="op">(</span>data_file <span class="op">=</span> <span class="st">"path/to/file"</span>, catalog_file <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span>
|
||||
|
||||
<span class="co"># read SAS transport files (version 5 and version 8):</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_xpt.html">read_xpt</a></span>(file = <span class="st">"path/to/file"</span>)
|
||||
</pre></div>
|
||||
<span class="fu">read_xpt</span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span></pre></div>
|
||||
<p>To export your R objects to the SAS file format:</p>
|
||||
<div class="sourceCode" id="cb7"><pre class="downlit">
|
||||
<span class="co"># save as regular SAS file:</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_sas.html">write_sas</a></span>(data = <span class="kw">yourdata</span>, path = <span class="st">"path/to/file"</span>)
|
||||
<span class="fu">write_sas</span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
|
||||
|
||||
<span class="co"># the SAS transport format is an open format </span>
|
||||
<span class="co"># (required for submission of the data to the FDA)</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_xpt.html">write_xpt</a></span>(data = <span class="kw">yourdata</span>, path = <span class="st">"path/to/file"</span>, version = <span class="fl">8</span>)
|
||||
</pre></div>
|
||||
<span class="fu">write_xpt</span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span>, version <span class="op">=</span> <span class="fl">8</span><span class="op">)</span></pre></div>
|
||||
</div>
|
||||
<div id="stata" class="section level4">
|
||||
<h4 class="hasAnchor">
|
||||
@ -392,17 +386,15 @@
|
||||
<p>To read files from Stata into R:</p>
|
||||
<div class="sourceCode" id="cb8"><pre class="downlit">
|
||||
<span class="co"># read .dta file:</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_dta.html">read_stata</a></span>(file = <span class="st">"/path/to/file"</span>)
|
||||
<span class="fu">read_stata</span><span class="op">(</span>file <span class="op">=</span> <span class="st">"/path/to/file"</span><span class="op">)</span>
|
||||
|
||||
<span class="co"># works exactly the same:</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_dta.html">read_dta</a></span>(file = <span class="st">"/path/to/file"</span>)
|
||||
</pre></div>
|
||||
<span class="fu">read_dta</span><span class="op">(</span>file <span class="op">=</span> <span class="st">"/path/to/file"</span><span class="op">)</span></pre></div>
|
||||
<p>To export your R objects to the Stata file format:</p>
|
||||
<div class="sourceCode" id="cb9"><pre class="downlit">
|
||||
<span class="co"># save as .dta file, Stata version 14:</span>
|
||||
<span class="co"># (supports Stata v8 until v15 at the time of writing)</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_dta.html">write_dta</a></span>(data = <span class="kw">yourdata</span>, path = <span class="st">"/path/to/file"</span>, version = <span class="fl">14</span>)
|
||||
</pre></div>
|
||||
<span class="fu">write_dta</span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"/path/to/file"</span>, version <span class="op">=</span> <span class="fl">14</span><span class="op">)</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
@ -423,7 +415,7 @@
|
||||
</div>
|
||||
|
||||
<div class="pkgdown">
|
||||
<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.9000.</p>
|
||||
<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
|
||||
</div>
|
||||
|
||||
</footer>
|
||||
|
||||
@ -0,0 +1,4 @@
|
||||
/* Styles for section anchors */
|
||||
a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
|
||||
a.anchor-section::before {content: '#';}
|
||||
.hasAnchor:hover a.anchor-section {visibility: visible;}
|
||||
@ -0,0 +1,33 @@
|
||||
// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
|
||||
document.addEventListener('DOMContentLoaded', function() {
|
||||
// Do nothing if AnchorJS is used
|
||||
if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
|
||||
return;
|
||||
}
|
||||
|
||||
const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
|
||||
|
||||
// Do nothing if sections are already anchored
|
||||
if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
|
||||
return null;
|
||||
}
|
||||
|
||||
// Use section id when pandoc runs with --section-divs
|
||||
const section_id = function(x) {
|
||||
return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
|
||||
? x.id : '');
|
||||
};
|
||||
|
||||
// Add anchors
|
||||
h.forEach(function(x) {
|
||||
const id = x.id || section_id(x.parentElement);
|
||||
if (id === '') {
|
||||
return null;
|
||||
}
|
||||
let anchor = document.createElement('a');
|
||||
anchor.href = '#' + id;
|
||||
anchor.classList = ['anchor-section'];
|
||||
x.classList.add('hasAnchor');
|
||||
x.appendChild(anchor);
|
||||
});
|
||||
});
|
||||
@ -39,7 +39,7 @@
|
||||
</button>
|
||||
<span class="navbar-brand">
|
||||
<a class="navbar-link" href="../index.html">AMR (for R)</a>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9008</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
@ -187,7 +187,8 @@
|
||||
|
||||
|
||||
|
||||
</header><script src="WHONET_files/header-attrs-2.3/header-attrs.js"></script><script src="WHONET_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
|
||||
</header><script src="WHONET_files/accessible-code-block-0.0.1/empty-anchor.js"></script><link href="WHONET_files/anchor-sections-1.0/anchor-sections.css" rel="stylesheet">
|
||||
<script src="WHONET_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row">
|
||||
<div class="col-md-9 contents">
|
||||
<div class="page-header toc-ignore">
|
||||
<h1 data-toc-skip>How to work with WHONET data</h1>
|
||||
@ -206,9 +207,8 @@
|
||||
<p>This tutorial assumes you already imported the WHONET data with e.g. the <a href="https://readxl.tidyverse.org/"><code>readxl</code> package</a>. In RStudio, this can be done using the menu button ‘Import Dataset’ in the tab ‘Environment’. Choose the option ‘From Excel’ and select your exported file. Make sure date fields are imported correctly.</p>
|
||||
<p>An example syntax could look like this:</p>
|
||||
<div class="sourceCode" id="cb1"><pre class="downlit">
|
||||
<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://readxl.tidyverse.org">readxl</a></span>)
|
||||
<span class="kw">data</span> <span class="op"><-</span> <span class="fu"><a href="https://readxl.tidyverse.org/reference/read_excel.html">read_excel</a></span>(path = <span class="st">"path/to/your/file.xlsx"</span>)
|
||||
</pre></div>
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://readxl.tidyverse.org">readxl</a></span><span class="op">)</span>
|
||||
<span class="va">data</span> <span class="op"><-</span> <span class="fu"><a href="https://readxl.tidyverse.org/reference/read_excel.html">read_excel</a></span><span class="op">(</span>path <span class="op">=</span> <span class="st">"path/to/your/file.xlsx"</span><span class="op">)</span></pre></div>
|
||||
<p>This package comes with an <a href="https://msberends.github.io/AMR/reference/WHONET.html">example data set <code>WHONET</code></a>. We will use it for this analysis.</p>
|
||||
</div>
|
||||
<div id="preparation" class="section level3">
|
||||
@ -216,11 +216,10 @@
|
||||
<a href="#preparation" class="anchor"></a>Preparation</h3>
|
||||
<p>First, load the relevant packages if you did not yet did this. I use the tidyverse for all of my analyses. All of them. If you don’t know it yet, I suggest you read about it on their website: <a href="https://www.tidyverse.org/" class="uri">https://www.tidyverse.org/</a>.</p>
|
||||
<div class="sourceCode" id="cb2"><pre class="downlit">
|
||||
<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://dplyr.tidyverse.org">dplyr</a></span>) <span class="co"># part of tidyverse</span>
|
||||
<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="http://ggplot2.tidyverse.org">ggplot2</a></span>) <span class="co"># part of tidyverse</span>
|
||||
<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://msberends.github.io/AMR">AMR</a></span>) <span class="co"># this package</span>
|
||||
<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://github.com/msberends/cleaner">cleaner</a></span>) <span class="co"># to create frequency tables</span>
|
||||
</pre></div>
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span>
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="http://ggplot2.tidyverse.org">ggplot2</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span>
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span> <span class="co"># this package</span>
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/msberends/cleaner">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></pre></div>
|
||||
<p>We will have to transform some variables to simplify and automate the analysis:</p>
|
||||
<ul>
|
||||
<li>Microorganisms should be transformed to our own microorganism IDs (called an <code>mo</code>) using <a href="https://msberends.github.io/AMR/reference/catalogue_of_life">our Catalogue of Life reference data set</a>, which contains all ~70,000 microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa. We do the tranformation with <code><a href="../reference/as.mo.html">as.mo()</a></code>. This function also recognises almost all WHONET abbreviations of microorganisms.</li>
|
||||
@ -228,19 +227,17 @@
|
||||
</ul>
|
||||
<div class="sourceCode" id="cb3"><pre class="downlit">
|
||||
<span class="co"># transform variables</span>
|
||||
<span class="kw">data</span> <span class="op"><-</span> <span class="kw">WHONET</span> <span class="op">%>%</span>
|
||||
<span class="va">data</span> <span class="op"><-</span> <span class="va">WHONET</span> <span class="op">%>%</span>
|
||||
<span class="co"># get microbial ID based on given organism</span>
|
||||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate</a></span>(mo = <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="kw">Organism</span>)) <span class="op">%>%</span>
|
||||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="va">Organism</span><span class="op">)</span><span class="op">)</span> <span class="op">%>%</span>
|
||||
<span class="co"># transform everything from "AMP_ND10" to "CIP_EE" to the new `rsi` class</span>
|
||||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html">mutate_at</a></span>(<span class="fu"><a href="https://dplyr.tidyverse.org/reference/vars.html">vars</a></span>(<span class="kw">AMP_ND10</span><span class="op">:</span><span class="kw">CIP_EE</span>), <span class="kw">as.rsi</span>)
|
||||
</pre></div>
|
||||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html">mutate_at</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/vars.html">vars</a></span><span class="op">(</span><span class="va">AMP_ND10</span><span class="op">:</span><span class="va">CIP_EE</span><span class="op">)</span>, <span class="va">as.rsi</span><span class="op">)</span></pre></div>
|
||||
<p>No errors or warnings, so all values are transformed succesfully.</p>
|
||||
<p>We also created a package dedicated to data cleaning and checking, called the <code>cleaner</code> package. Its <code><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq()</a></code> function can be used to create frequency tables.</p>
|
||||
<p>So let’s check our data, with a couple of frequency tables:</p>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit">
|
||||
<span class="co"># our newly created `mo` variable, put in the mo_name() function</span>
|
||||
<span class="kw">data</span> <span class="op">%>%</span> <span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span>(<span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="kw">mo</span>), nmax = <span class="fl">10</span>)
|
||||
</pre></div>
|
||||
<span class="va">data</span> <span class="op">%>%</span> <span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, nmax <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></pre></div>
|
||||
<p><strong>Frequency table</strong></p>
|
||||
<p>Class: character<br>
|
||||
Length: 500<br>
|
||||
@ -344,15 +341,16 @@ Longest: 40</p>
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit">
|
||||
<span class="co"># our transformed antibiotic columns</span>
|
||||
<span class="co"># amoxicillin/clavulanic acid (J01CR02) as an example</span>
|
||||
<span class="kw">data</span> <span class="op">%>%</span> <span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span>(<span class="kw">AMC_ND2</span>)
|
||||
</pre></div>
|
||||
<span class="va">data</span> <span class="op">%>%</span> <span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span><span class="op">(</span><span class="va">AMC_ND2</span><span class="op">)</span></pre></div>
|
||||
<p><strong>Frequency table</strong></p>
|
||||
<p>Class: factor > ordered > rsi (numeric)<br>
|
||||
Length: 500<br>
|
||||
Levels: 3: S < I < R<br>
|
||||
Available: 481 (96.2%, NA: 19 = 3.8%)<br>
|
||||
Unique: 3</p>
|
||||
<p>%SI: 78.59%</p>
|
||||
<p>Drug: Amoxicillin/clavulanic acid (AMC, J01CR02)<br>
|
||||
Drug group: Beta-lactams/penicillins<br>
|
||||
%SI: 78.59%</p>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
<th align="left"></th>
|
||||
@ -395,11 +393,10 @@ Unique: 3</p>
|
||||
<a href="#a-first-glimpse-at-results" class="anchor"></a>A first glimpse at results</h3>
|
||||
<p>An easy <code>ggplot</code> will already give a lot of information, using the included <code><a href="../reference/ggplot_rsi.html">ggplot_rsi()</a></code> function:</p>
|
||||
<div class="sourceCode" id="cb6"><pre class="downlit">
|
||||
<span class="kw">data</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span>(<span class="kw">Country</span>) <span class="op">%>%</span>
|
||||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span>(<span class="kw">Country</span>, <span class="kw">AMP_ND2</span>, <span class="kw">AMC_ED20</span>, <span class="kw">CAZ_ED10</span>, <span class="kw">CIP_ED5</span>) <span class="op">%>%</span>
|
||||
<span class="fu"><a href="../reference/ggplot_rsi.html">ggplot_rsi</a></span>(translate_ab = <span class="st">'ab'</span>, facet = <span class="st">"Country"</span>, datalabels = <span class="fl">FALSE</span>)
|
||||
</pre></div>
|
||||
<span class="va">data</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span><span class="op">(</span><span class="va">Country</span><span class="op">)</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span><span class="op">(</span><span class="va">Country</span>, <span class="va">AMP_ND2</span>, <span class="va">AMC_ED20</span>, <span class="va">CAZ_ED10</span>, <span class="va">CIP_ED5</span><span class="op">)</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="../reference/ggplot_rsi.html">ggplot_rsi</a></span><span class="op">(</span>translate_ab <span class="op">=</span> <span class="st">'ab'</span>, facet <span class="op">=</span> <span class="st">"Country"</span>, datalabels <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></pre></div>
|
||||
<p><img src="WHONET_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
|
||||
</div>
|
||||
</div>
|
||||
@ -417,7 +414,7 @@ Unique: 3</p>
|
||||
</div>
|
||||
|
||||
<div class="pkgdown">
|
||||
<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.9000.</p>
|
||||
<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
|
||||
</div>
|
||||
|
||||
</footer>
|
||||
|
||||
@ -0,0 +1,4 @@
|
||||
/* Styles for section anchors */
|
||||
a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
|
||||
a.anchor-section::before {content: '#';}
|
||||
.hasAnchor:hover a.anchor-section {visibility: visible;}
|
||||
@ -0,0 +1,33 @@
|
||||
// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
|
||||
document.addEventListener('DOMContentLoaded', function() {
|
||||
// Do nothing if AnchorJS is used
|
||||
if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
|
||||
return;
|
||||
}
|
||||
|
||||
const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
|
||||
|
||||
// Do nothing if sections are already anchored
|
||||
if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
|
||||
return null;
|
||||
}
|
||||
|
||||
// Use section id when pandoc runs with --section-divs
|
||||
const section_id = function(x) {
|
||||
return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
|
||||
? x.id : '');
|
||||
};
|
||||
|
||||
// Add anchors
|
||||
h.forEach(function(x) {
|
||||
const id = x.id || section_id(x.parentElement);
|
||||
if (id === '') {
|
||||
return null;
|
||||
}
|
||||
let anchor = document.createElement('a');
|
||||
anchor.href = '#' + id;
|
||||
anchor.classList = ['anchor-section'];
|
||||
x.classList.add('hasAnchor');
|
||||
x.appendChild(anchor);
|
||||
});
|
||||
});
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 94 KiB After Width: | Height: | Size: 60 KiB |
@ -39,7 +39,7 @@
|
||||
</button>
|
||||
<span class="navbar-brand">
|
||||
<a class="navbar-link" href="../index.html">AMR (for R)</a>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9000</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9008</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
@ -187,7 +187,8 @@
|
||||
|
||||
|
||||
|
||||
</header><script src="benchmarks_files/header-attrs-2.4/header-attrs.js"></script><script src="benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
|
||||
</header><script src="benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js"></script><link href="benchmarks_files/anchor-sections-1.0/anchor-sections.css" rel="stylesheet">
|
||||
<script src="benchmarks_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row">
|
||||
<div class="col-md-9 contents">
|
||||
<div class="page-header toc-ignore">
|
||||
<h1 data-toc-skip>Benchmarks</h1>
|
||||
@ -227,20 +228,34 @@
|
||||
times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span>
|
||||
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">S.aureus</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>
|
||||
<span class="co"># Unit: milliseconds</span>
|
||||
<span class="co"># expr min lq mean median uq max neval</span>
|
||||
<span class="co"># as.mo("sau") 12.0 13.0 25.0 15.0 40.0 43 10</span>
|
||||
<span class="co"># as.mo("stau") 210.0 220.0 230.0 230.0 250.0 260 10</span>
|
||||
<span class="co"># as.mo("STAU") 210.0 210.0 230.0 250.0 250.0 260 10</span>
|
||||
<span class="co"># as.mo("staaur") 12.0 13.0 24.0 14.0 41.0 42 10</span>
|
||||
<span class="co"># as.mo("STAAUR") 13.0 13.0 22.0 14.0 39.0 42 10</span>
|
||||
<span class="co"># as.mo("S. aureus") 27.0 30.0 42.0 41.0 54.0 61 10</span>
|
||||
<span class="co"># as.mo("S aureus") 24.0 29.0 45.0 43.0 60.0 66 10</span>
|
||||
<span class="co"># as.mo("Staphylococcus aureus") 2.1 2.5 4.9 2.5 2.9 26 10</span>
|
||||
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 820.0 860.0 880.0 880.0 900.0 970 10</span>
|
||||
<span class="co"># as.mo("Sthafilokkockus aaureuz") 330.0 340.0 370.0 370.0 390.0 420 10</span>
|
||||
<span class="co"># as.mo("MRSA") 11.0 13.0 22.0 14.0 41.0 44 10</span>
|
||||
<span class="co"># as.mo("VISA") 21.0 22.0 48.0 47.0 51.0 140 10</span>
|
||||
<span class="co"># as.mo("VRSA") 19.0 20.0 23.0 21.0 22.0 47 10</span></pre></div>
|
||||
<span class="co"># expr min lq mean median uq max</span>
|
||||
<span class="co"># as.mo("sau") 26.0 26.0 50.0 26.0 26.0 180</span>
|
||||
<span class="co"># as.mo("stau") 430.0 430.0 470.0 440.0 490.0 630</span>
|
||||
<span class="co"># as.mo("STAU") 430.0 440.0 470.0 500.0 500.0 500</span>
|
||||
<span class="co"># as.mo("staaur") 26.0 26.0 32.0 26.0 26.0 76</span>
|
||||
<span class="co"># as.mo("STAAUR") 26.0 26.0 44.0 26.0 81.0 83</span>
|
||||
<span class="co"># as.mo("S. aureus") 60.0 60.0 77.0 61.0 110.0 120</span>
|
||||
<span class="co"># as.mo("S aureus") 60.0 61.0 94.0 110.0 120.0 120</span>
|
||||
<span class="co"># as.mo("Staphylococcus aureus") 4.1 4.1 9.6 4.1 4.2 59</span>
|
||||
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 2200.0 2200.0 2200.0 2200.0 2300.0 2300</span>
|
||||
<span class="co"># as.mo("Sthafilokkockus aaureuz") 760.0 800.0 810.0 800.0 810.0 840</span>
|
||||
<span class="co"># as.mo("MRSA") 26.0 26.0 37.0 26.0 26.0 83</span>
|
||||
<span class="co"># as.mo("VISA") 42.0 42.0 59.0 42.0 92.0 97</span>
|
||||
<span class="co"># as.mo("VRSA") 42.0 42.0 68.0 67.0 92.0 97</span>
|
||||
<span class="co"># neval</span>
|
||||
<span class="co"># 10</span>
|
||||
<span class="co"># 10</span>
|
||||
<span class="co"># 10</span>
|
||||
<span class="co"># 10</span>
|
||||
<span class="co"># 10</span>
|
||||
<span class="co"># 10</span>
|
||||
<span class="co"># 10</span>
|
||||
<span class="co"># 10</span>
|
||||
<span class="co"># 10</span>
|
||||
<span class="co"># 10</span>
|
||||
<span class="co"># 10</span>
|
||||
<span class="co"># 10</span>
|
||||
<span class="co"># 10</span></pre></div>
|
||||
<p><img src="benchmarks_files/figure-html/unnamed-chunk-4-1.png" width="562.5"></p>
|
||||
<p>In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 100 milliseconds, this is only 10 input values per second. It is clear that accepted taxonomic names are extremely fast, but some variations can take up to 500-1000 times as much time.</p>
|
||||
<p>To improve performance, two important calculations take almost no time at all: <strong>repetitive results</strong> and <strong>already precalculated results</strong>.</p>
|
||||
@ -270,8 +285,8 @@
|
||||
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
|
||||
<span class="co"># Unit: milliseconds</span>
|
||||
<span class="co"># expr min lq mean median uq max neval</span>
|
||||
<span class="co"># mo_name(x) 150 154 195 179 197 301 10</span></pre></div>
|
||||
<p>So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.179 seconds. You only lose time on your unique input values.</p>
|
||||
<span class="co"># mo_name(x) 308 326 399 331 370 676 10</span></pre></div>
|
||||
<p>So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.331 seconds. You only lose time on your unique input values.</p>
|
||||
</div>
|
||||
<div id="precalculated-results" class="section level3">
|
||||
<h3 class="hasAnchor">
|
||||
@ -284,11 +299,11 @@
|
||||
times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span>
|
||||
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
|
||||
<span class="co"># Unit: milliseconds</span>
|
||||
<span class="co"># expr min lq mean median uq max neval</span>
|
||||
<span class="co"># A 7.11 7.23 7.79 7.74 8.46 8.54 10</span>
|
||||
<span class="co"># B 21.40 21.90 30.40 24.00 25.50 60.00 10</span>
|
||||
<span class="co"># C 1.97 1.98 2.12 2.05 2.34 2.43 10</span></pre></div>
|
||||
<p>So going from <code><a href="../reference/mo_property.html">mo_name("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.002 seconds - it doesn’t even start calculating <em>if the result would be the same as the expected resulting value</em>. That goes for all helper functions:</p>
|
||||
<span class="co"># expr min lq mean median uq max neval</span>
|
||||
<span class="co"># A 16.20 16.20 18.60 17.90 18.90 28.00 10</span>
|
||||
<span class="co"># B 50.90 51.10 57.70 51.20 53.10 108.00 10</span>
|
||||
<span class="co"># C 3.58 3.72 4.03 4.06 4.14 4.91 10</span></pre></div>
|
||||
<p>So going from <code><a href="../reference/mo_property.html">mo_name("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0041 seconds - it doesn’t even start calculating <em>if the result would be the same as the expected resulting value</em>. That goes for all helper functions:</p>
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit">
|
||||
<span class="va">run_it</span> <span class="op"><-</span> <span class="fu">microbenchmark</span><span class="op">(</span>A <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_species</a></span><span class="op">(</span><span class="st">"aureus"</span><span class="op">)</span>,
|
||||
B <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="st">"Staphylococcus"</span><span class="op">)</span>,
|
||||
@ -301,15 +316,15 @@
|
||||
times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span>
|
||||
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
|
||||
<span class="co"># Unit: milliseconds</span>
|
||||
<span class="co"># expr min lq mean median uq max neval</span>
|
||||
<span class="co"># A 1.33 1.57 1.70 1.69 1.83 2.18 10</span>
|
||||
<span class="co"># B 1.62 1.67 5.34 1.70 2.15 36.90 10</span>
|
||||
<span class="co"># C 1.43 1.55 1.64 1.59 1.70 2.06 10</span>
|
||||
<span class="co"># D 1.29 1.44 1.53 1.55 1.66 1.70 10</span>
|
||||
<span class="co"># E 1.31 1.58 1.68 1.65 1.71 2.11 10</span>
|
||||
<span class="co"># F 1.54 1.61 1.80 1.67 1.86 2.57 10</span>
|
||||
<span class="co"># G 1.52 1.62 1.73 1.71 1.77 2.04 10</span>
|
||||
<span class="co"># H 1.40 1.55 1.70 1.65 1.92 2.03 10</span></pre></div>
|
||||
<span class="co"># expr min lq mean median uq max neval</span>
|
||||
<span class="co"># A 3.62 3.62 3.66 3.64 3.67 3.80 10</span>
|
||||
<span class="co"># B 3.57 3.59 3.68 3.64 3.72 4.01 10</span>
|
||||
<span class="co"># C 3.63 3.64 3.65 3.64 3.67 3.71 10</span>
|
||||
<span class="co"># D 3.57 3.58 3.62 3.61 3.67 3.69 10</span>
|
||||
<span class="co"># E 3.56 3.57 3.60 3.59 3.60 3.68 10</span>
|
||||
<span class="co"># F 3.55 3.58 3.73 3.65 3.85 4.01 10</span>
|
||||
<span class="co"># G 3.54 3.56 3.62 3.58 3.61 3.93 10</span>
|
||||
<span class="co"># H 3.53 3.57 3.59 3.58 3.63 3.66 10</span></pre></div>
|
||||
<p>Of course, when running <code><a href="../reference/mo_property.html">mo_phylum("Firmicutes")</a></code> the function has zero knowledge about the actual microorganism, namely <em>S. aureus</em>. But since the result would be <code>"Firmicutes"</code> anyway, there is no point in calculating the result. And because this package ‘knows’ all phyla of all known bacteria (according to the Catalogue of Life), it can just return the initial value immediately.</p>
|
||||
</div>
|
||||
<div id="results-in-other-languages" class="section level3">
|
||||
@ -336,14 +351,14 @@
|
||||
times <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>
|
||||
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">4</span><span class="op">)</span>
|
||||
<span class="co"># Unit: milliseconds</span>
|
||||
<span class="co"># expr min lq mean median uq max neval</span>
|
||||
<span class="co"># en 14.20 14.66 20.04 15.14 16.90 58.67 100</span>
|
||||
<span class="co"># de 17.15 17.85 24.30 18.27 20.00 59.17 100</span>
|
||||
<span class="co"># nl 29.13 30.50 34.60 31.26 32.86 72.63 100</span>
|
||||
<span class="co"># es 16.95 17.70 22.61 18.35 20.54 59.42 100</span>
|
||||
<span class="co"># it 17.04 17.60 21.72 18.14 19.60 59.19 100</span>
|
||||
<span class="co"># fr 16.82 17.52 21.71 18.05 19.63 61.06 100</span>
|
||||
<span class="co"># pt 16.92 17.58 19.92 18.23 19.19 54.61 100</span></pre></div>
|
||||
<span class="co"># expr min lq mean median uq max neval</span>
|
||||
<span class="co"># en 35.85 36.13 41.92 36.29 36.84 318.20 100</span>
|
||||
<span class="co"># de 42.37 42.67 45.11 42.91 43.19 95.04 100</span>
|
||||
<span class="co"># nl 70.13 70.65 75.92 70.95 71.50 140.20 100</span>
|
||||
<span class="co"># es 42.16 42.46 46.88 42.64 43.20 95.37 100</span>
|
||||
<span class="co"># it 41.85 42.24 46.84 42.40 42.84 95.92 100</span>
|
||||
<span class="co"># fr 42.13 42.53 48.41 42.71 43.27 95.21 100</span>
|
||||
<span class="co"># pt 42.25 42.47 48.56 42.71 43.12 96.60 100</span></pre></div>
|
||||
<p>Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.</p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@ -0,0 +1,4 @@
|
||||
/* Styles for section anchors */
|
||||
a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
|
||||
a.anchor-section::before {content: '#';}
|
||||
.hasAnchor:hover a.anchor-section {visibility: visible;}
|
||||
@ -0,0 +1,33 @@
|
||||
// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
|
||||
document.addEventListener('DOMContentLoaded', function() {
|
||||
// Do nothing if AnchorJS is used
|
||||
if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
|
||||
return;
|
||||
}
|
||||
|
||||
const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
|
||||
|
||||
// Do nothing if sections are already anchored
|
||||
if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
|
||||
return null;
|
||||
}
|
||||
|
||||
// Use section id when pandoc runs with --section-divs
|
||||
const section_id = function(x) {
|
||||
return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
|
||||
? x.id : '');
|
||||
};
|
||||
|
||||
// Add anchors
|
||||
h.forEach(function(x) {
|
||||
const id = x.id || section_id(x.parentElement);
|
||||
if (id === '') {
|
||||
return null;
|
||||
}
|
||||
let anchor = document.createElement('a');
|
||||
anchor.href = '#' + id;
|
||||
anchor.classList = ['anchor-section'];
|
||||
x.classList.add('hasAnchor');
|
||||
x.appendChild(anchor);
|
||||
});
|
||||
});
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 69 KiB After Width: | Height: | Size: 70 KiB |
@ -81,7 +81,7 @@
|
||||
</button>
|
||||
<span class="navbar-brand">
|
||||
<a class="navbar-link" href="../index.html">AMR (for R)</a>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9007</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9008</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
|
||||
@ -39,7 +39,7 @@
|
||||
</button>
|
||||
<span class="navbar-brand">
|
||||
<a class="navbar-link" href="../index.html">AMR (for R)</a>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9008</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
@ -187,7 +187,8 @@
|
||||
|
||||
|
||||
|
||||
</header><script src="resistance_predict_files/header-attrs-2.3/header-attrs.js"></script><script src="resistance_predict_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
|
||||
</header><script src="resistance_predict_files/accessible-code-block-0.0.1/empty-anchor.js"></script><link href="resistance_predict_files/anchor-sections-1.0/anchor-sections.css" rel="stylesheet">
|
||||
<script src="resistance_predict_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row">
|
||||
<div class="col-md-9 contents">
|
||||
<div class="page-header toc-ignore">
|
||||
<h1 data-toc-skip>How to predict antimicrobial resistance</h1>
|
||||
@ -206,13 +207,12 @@
|
||||
<p>As with many uses in R, we need some additional packages for AMR analysis. Our package works closely together with the <a href="https://www.tidyverse.org">tidyverse packages</a> <a href="https://dplyr.tidyverse.org/"><code>dplyr</code></a> and <a href="https://ggplot2.tidyverse.org"><code>ggplot2</code></a> by Dr Hadley Wickham. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.</p>
|
||||
<p>Our <code>AMR</code> package depends on these packages and even extends their use and functions.</p>
|
||||
<div class="sourceCode" id="cb1"><pre class="downlit">
|
||||
<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://dplyr.tidyverse.org">dplyr</a></span>)
|
||||
<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="http://ggplot2.tidyverse.org">ggplot2</a></span>)
|
||||
<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://msberends.github.io/AMR">AMR</a></span>)
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span>
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="http://ggplot2.tidyverse.org">ggplot2</a></span><span class="op">)</span>
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
|
||||
|
||||
<span class="co"># (if not yet installed, install with:)</span>
|
||||
<span class="co"># install.packages(c("tidyverse", "AMR"))</span>
|
||||
</pre></div>
|
||||
<span class="co"># install.packages(c("tidyverse", "AMR"))</span></pre></div>
|
||||
</div>
|
||||
<div id="prediction-analysis" class="section level2">
|
||||
<h2 class="hasAnchor">
|
||||
@ -236,7 +236,7 @@
|
||||
<pre><code># NOTE: Using column `date` as input for `col_date`.</code></pre>
|
||||
<p>This text is only a printed summary - the actual result (output) of the function is a <code>data.frame</code> containing for each year: the number of observations, the actual observed resistance, the estimated resistance and the standard error below and above the estimation:</p>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit">
|
||||
<span class="kw">predict_TZP</span>
|
||||
<span class="va">predict_TZP</span>
|
||||
<span class="co"># year value se_min se_max observations observed estimated</span>
|
||||
<span class="co"># 1 2002 0.20000000 NA NA 15 0.20000000 0.05616378</span>
|
||||
<span class="co"># 2 2003 0.06250000 NA NA 32 0.06250000 0.06163839</span>
|
||||
@ -266,36 +266,31 @@
|
||||
<span class="co"># 26 2027 0.41315710 0.3244399 0.5018743 NA NA 0.41315710</span>
|
||||
<span class="co"># 27 2028 0.43730688 0.3418075 0.5328063 NA NA 0.43730688</span>
|
||||
<span class="co"># 28 2029 0.46175755 0.3597639 0.5637512 NA NA 0.46175755</span>
|
||||
<span class="co"># 29 2030 0.48639359 0.3782932 0.5944939 NA NA 0.48639359</span>
|
||||
</pre></div>
|
||||
<span class="co"># 29 2030 0.48639359 0.3782932 0.5944939 NA NA 0.48639359</span></pre></div>
|
||||
<p>The function <code>plot</code> is available in base R, and can be extended by other packages to depend the output based on the type of input. We extended its function to cope with resistance predictions:</p>
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit">
|
||||
<span class="fu"><a href="../reference/plot.html">plot</a></span>(<span class="kw">predict_TZP</span>)
|
||||
</pre></div>
|
||||
<span class="fu"><a href="../reference/plot.html">plot</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></pre></div>
|
||||
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-4-1.png" width="720"></p>
|
||||
<p>This is the fastest way to plot the result. It automatically adds the right axes, error bars, titles, number of available observations and type of model.</p>
|
||||
<p>We also support the <code>ggplot2</code> package with our custom function <code><a href="../reference/resistance_predict.html">ggplot_rsi_predict()</a></code> to create more appealing plots:</p>
|
||||
<div class="sourceCode" id="cb6"><pre class="downlit">
|
||||
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>(<span class="kw">predict_TZP</span>)
|
||||
</pre></div>
|
||||
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></pre></div>
|
||||
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-1.png" width="720"></p>
|
||||
<div class="sourceCode" id="cb7"><pre class="downlit">
|
||||
|
||||
<span class="co"># choose for error bars instead of a ribbon</span>
|
||||
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>(<span class="kw">predict_TZP</span>, ribbon = <span class="fl">FALSE</span>)
|
||||
</pre></div>
|
||||
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span>, ribbon <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></pre></div>
|
||||
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-2.png" width="720"></p>
|
||||
<div id="choosing-the-right-model" class="section level3">
|
||||
<h3 class="hasAnchor">
|
||||
<a href="#choosing-the-right-model" class="anchor"></a>Choosing the right model</h3>
|
||||
<p>Resistance is not easily predicted; if we look at vancomycin resistance in Gram-positive bacteria, the spread (i.e. standard error) is enormous:</p>
|
||||
<div class="sourceCode" id="cb8"><pre class="downlit">
|
||||
<span class="kw">example_isolates</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(<span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span>(<span class="kw">mo</span>, language = <span class="kw">NULL</span>) <span class="op">==</span> <span class="st">"Gram-positive"</span>) <span class="op">%>%</span>
|
||||
<span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(col_ab = <span class="st">"VAN"</span>, year_min = <span class="fl">2010</span>, info = <span class="fl">FALSE</span>, model = <span class="st">"binomial"</span>) <span class="op">%>%</span>
|
||||
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>()
|
||||
<span class="co"># NOTE: Using column `date` as input for `col_date`.</span>
|
||||
</pre></div>
|
||||
<span class="va">example_isolates</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span>, language <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Gram-positive"</span><span class="op">)</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span>col_ab <span class="op">=</span> <span class="st">"VAN"</span>, year_min <span class="op">=</span> <span class="fl">2010</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, model <span class="op">=</span> <span class="st">"binomial"</span><span class="op">)</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="op">)</span>
|
||||
<span class="co"># NOTE: Using column `date` as input for `col_date`.</span></pre></div>
|
||||
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-6-1.png" width="720"></p>
|
||||
<p>Vancomycin resistance could be 100% in ten years, but might also stay around 0%.</p>
|
||||
<p>You can define the model with the <code>model</code> parameter. The model chosen above is a generalised linear regression model using a binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance.</p>
|
||||
@ -337,28 +332,26 @@
|
||||
</table>
|
||||
<p>For the vancomycin resistance in Gram-positive bacteria, a linear model might be more appropriate since no binomial distribution is to be expected based on the observed years:</p>
|
||||
<div class="sourceCode" id="cb9"><pre class="downlit">
|
||||
<span class="kw">example_isolates</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(<span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span>(<span class="kw">mo</span>, language = <span class="kw">NULL</span>) <span class="op">==</span> <span class="st">"Gram-positive"</span>) <span class="op">%>%</span>
|
||||
<span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(col_ab = <span class="st">"VAN"</span>, year_min = <span class="fl">2010</span>, info = <span class="fl">FALSE</span>, model = <span class="st">"linear"</span>) <span class="op">%>%</span>
|
||||
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>()
|
||||
<span class="co"># NOTE: Using column `date` as input for `col_date`.</span>
|
||||
</pre></div>
|
||||
<span class="va">example_isolates</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span>, language <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Gram-positive"</span><span class="op">)</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span>col_ab <span class="op">=</span> <span class="st">"VAN"</span>, year_min <span class="op">=</span> <span class="fl">2010</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, model <span class="op">=</span> <span class="st">"linear"</span><span class="op">)</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="op">)</span>
|
||||
<span class="co"># NOTE: Using column `date` as input for `col_date`.</span></pre></div>
|
||||
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
|
||||
<p>This seems more likely, doesn’t it?</p>
|
||||
<p>The model itself is also available from the object, as an <code>attribute</code>:</p>
|
||||
<div class="sourceCode" id="cb10"><pre class="downlit">
|
||||
<span class="kw">model</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/attributes.html">attributes</a></span>(<span class="kw">predict_TZP</span>)<span class="op">$</span><span class="kw">model</span>
|
||||
<span class="va">model</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/attributes.html">attributes</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span><span class="op">$</span><span class="va">model</span>
|
||||
|
||||
<span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(<span class="kw">model</span>)<span class="op">$</span><span class="kw">family</span>
|
||||
<span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span><span class="op">$</span><span class="va">family</span>
|
||||
<span class="co"># </span>
|
||||
<span class="co"># Family: binomial </span>
|
||||
<span class="co"># Link function: logit</span>
|
||||
|
||||
<span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(<span class="kw">model</span>)<span class="op">$</span><span class="kw">coefficients</span>
|
||||
<span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span><span class="op">$</span><span class="va">coefficients</span>
|
||||
<span class="co"># Estimate Std. Error z value Pr(>|z|)</span>
|
||||
<span class="co"># (Intercept) -200.67944891 46.17315349 -4.346237 1.384932e-05</span>
|
||||
<span class="co"># year 0.09883005 0.02295317 4.305725 1.664395e-05</span>
|
||||
</pre></div>
|
||||
<span class="co"># year 0.09883005 0.02295317 4.305725 1.664395e-05</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
@ -378,7 +371,7 @@
|
||||
</div>
|
||||
|
||||
<div class="pkgdown">
|
||||
<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.9000.</p>
|
||||
<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
|
||||
</div>
|
||||
|
||||
</footer>
|
||||
|
||||
@ -0,0 +1,4 @@
|
||||
/* Styles for section anchors */
|
||||
a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
|
||||
a.anchor-section::before {content: '#';}
|
||||
.hasAnchor:hover a.anchor-section {visibility: visible;}
|
||||
@ -0,0 +1,33 @@
|
||||
// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
|
||||
document.addEventListener('DOMContentLoaded', function() {
|
||||
// Do nothing if AnchorJS is used
|
||||
if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
|
||||
return;
|
||||
}
|
||||
|
||||
const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
|
||||
|
||||
// Do nothing if sections are already anchored
|
||||
if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
|
||||
return null;
|
||||
}
|
||||
|
||||
// Use section id when pandoc runs with --section-divs
|
||||
const section_id = function(x) {
|
||||
return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
|
||||
? x.id : '');
|
||||
};
|
||||
|
||||
// Add anchors
|
||||
h.forEach(function(x) {
|
||||
const id = x.id || section_id(x.parentElement);
|
||||
if (id === '') {
|
||||
return null;
|
||||
}
|
||||
let anchor = document.createElement('a');
|
||||
anchor.href = '#' + id;
|
||||
anchor.classList = ['anchor-section'];
|
||||
x.classList.add('hasAnchor');
|
||||
x.appendChild(anchor);
|
||||
});
|
||||
});
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 96 KiB After Width: | Height: | Size: 48 KiB |
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|
Before Width: | Height: | Size: 94 KiB After Width: | Height: | Size: 70 KiB |
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|
Before Width: | Height: | Size: 92 KiB After Width: | Height: | Size: 69 KiB |
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|
Before Width: | Height: | Size: 94 KiB After Width: | Height: | Size: 72 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 88 KiB After Width: | Height: | Size: 65 KiB |
@ -39,7 +39,7 @@
|
||||
</button>
|
||||
<span class="navbar-brand">
|
||||
<a class="navbar-link" href="../index.html">AMR (for R)</a>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9008</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
@ -187,7 +187,8 @@
|
||||
|
||||
|
||||
|
||||
</header><script src="welcome_to_AMR_files/header-attrs-2.3/header-attrs.js"></script><script src="welcome_to_AMR_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
|
||||
</header><script src="welcome_to_AMR_files/accessible-code-block-0.0.1/empty-anchor.js"></script><link href="welcome_to_AMR_files/anchor-sections-1.0/anchor-sections.css" rel="stylesheet">
|
||||
<script src="welcome_to_AMR_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row">
|
||||
<div class="col-md-9 contents">
|
||||
<div class="page-header toc-ignore">
|
||||
<h1 data-toc-skip>Welcome to the AMR package</h1>
|
||||
@ -249,7 +250,7 @@
|
||||
</div>
|
||||
|
||||
<div class="pkgdown">
|
||||
<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.9000.</p>
|
||||
<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
|
||||
</div>
|
||||
|
||||
</footer>
|
||||
|
||||
@ -0,0 +1,4 @@
|
||||
/* Styles for section anchors */
|
||||
a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
|
||||
a.anchor-section::before {content: '#';}
|
||||
.hasAnchor:hover a.anchor-section {visibility: visible;}
|
||||
@ -0,0 +1,33 @@
|
||||
// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
|
||||
document.addEventListener('DOMContentLoaded', function() {
|
||||
// Do nothing if AnchorJS is used
|
||||
if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
|
||||
return;
|
||||
}
|
||||
|
||||
const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
|
||||
|
||||
// Do nothing if sections are already anchored
|
||||
if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
|
||||
return null;
|
||||
}
|
||||
|
||||
// Use section id when pandoc runs with --section-divs
|
||||
const section_id = function(x) {
|
||||
return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
|
||||
? x.id : '');
|
||||
};
|
||||
|
||||
// Add anchors
|
||||
h.forEach(function(x) {
|
||||
const id = x.id || section_id(x.parentElement);
|
||||
if (id === '') {
|
||||
return null;
|
||||
}
|
||||
let anchor = document.createElement('a');
|
||||
anchor.href = '#' + id;
|
||||
anchor.classList = ['anchor-section'];
|
||||
x.classList.add('hasAnchor');
|
||||
x.appendChild(anchor);
|
||||
});
|
||||
});
|
||||
Reference in New Issue
Block a user